Causal Inference, is just Inference: A beautifully simple idea that not everyone accepts

David Rohde
Proceedings on "I (Still) Can't Believe It's Not Better!" at NeurIPS 2021 Workshops, PMLR 163:75-79, 2022.

Abstract

It is often argued that causal inference is a step that follows probabilistic estimation in a two step procedure, with a separate statistical estimation and causal inference step and each step is governed by its own principles. We have argued to the contrary that Bayesian decision theory is perfectly adequate to do causal inference in a single step using nothing more than Bayesian conditioning. If true this formulation greatly simplifies causal inference. We outline this beautifully simple idea and discuss why some object to it.

Cite this Paper

BibTeX
@InProceedings{pmlr-v163-rohde22a, title = {Causal Inference, is just Inference: A beautifully simple idea that not everyone accepts}, author = {Rohde, David}, booktitle = {Proceedings on "I (Still) Can't Believe It's Not Better!" at NeurIPS 2021 Workshops}, pages = {75--79}, year = {2022}, editor = {Pradier, Melanie F. and Schein, Aaron and Hyland, Stephanie and Ruiz, Francisco J. R. and Forde, Jessica Z.}, volume = {163}, series = {Proceedings of Machine Learning Research}, month = {13 Dec}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v163/rohde22a/rohde22a.pdf}, url = {https://proceedings.mlr.press/v163/rohde22a.html}, abstract = {It is often argued that causal inference is a step that follows probabilistic estimation in a two step procedure, with a separate statistical estimation and causal inference step and each step is governed by its own principles. We have argued to the contrary that Bayesian decision theory is perfectly adequate to do causal inference in a single step using nothing more than Bayesian conditioning. If true this formulation greatly simplifies causal inference. We outline this beautifully simple idea and discuss why some object to it.} }
Endnote
%0 Conference Paper %T Causal Inference, is just Inference: A beautifully simple idea that not everyone accepts %A David Rohde %B Proceedings on "I (Still) Can't Believe It's Not Better!" at NeurIPS 2021 Workshops %C Proceedings of Machine Learning Research %D 2022 %E Melanie F. Pradier %E Aaron Schein %E Stephanie Hyland %E Francisco J. R. Ruiz %E Jessica Z. Forde %F pmlr-v163-rohde22a %I PMLR %P 75--79 %U https://proceedings.mlr.press/v163/rohde22a.html %V 163 %X It is often argued that causal inference is a step that follows probabilistic estimation in a two step procedure, with a separate statistical estimation and causal inference step and each step is governed by its own principles. We have argued to the contrary that Bayesian decision theory is perfectly adequate to do causal inference in a single step using nothing more than Bayesian conditioning. If true this formulation greatly simplifies causal inference. We outline this beautifully simple idea and discuss why some object to it.
APA
Rohde, D.. (2022). Causal Inference, is just Inference: A beautifully simple idea that not everyone accepts. Proceedings on "I (Still) Can't Believe It's Not Better!" at NeurIPS 2021 Workshops, in Proceedings of Machine Learning Research 163:75-79 Available from https://proceedings.mlr.press/v163/rohde22a.html.

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